This report is based on a model presented at the 2021 International Conference on Evolving Cities, University of Southampton, 22 – 24 September 2021.
If you are interested in how the model works start from https://dataknut.github.io/localCarbonTaxModels/
The model is similar in some respects to Evans et al (2023) Getting to net zero: Islington’s social housing stock (https://journal-buildingscities.org/articles/10.5334/bc.349) which estimated an overall cost of upgrading Islington’s social housing stock of 4500 buildings containing some 33,300 dwellings to be £1,600m.
A paper based on this model as applied to the City of Southampton has been published in Buildings & Cities:
If you wish to re-use material from this report please cite it as:
Ben Anderson (2023) Simulating a local emissions levy to fund local energy efficiency retrofit: Mid Suffolk as a case study. University of Southampton, United Kingdom
License: CC-BY
Share, adapt, give attribution.
This report estimates the value of an emissions levy for Mid Suffolk using LSOA level data on emissions derived from the CREDS place-based emissions calculator. These emissions are all consumption, gas and electricity. It does this under two scenarios - a simple carbon value multiplier and a rising block tariff.
It then compares these with estimates of the cost of retrofitting EPC band dwellings D-E and F-G in each LSOA and for the whole area under study.
Key results:
Section 10 gives the overall summary of the annual £ emissions levy value and estimated retrofit costs
This report estimates a model of an emissions levy for Mid Suffolk using LSOA level data on emissions derived from the CREDS place-based emissions calculator.
The model applies carbon ‘values’ to a number of emissions categories to estimate the levy revenue that would be generated for each LSOA in year 1 of such a levy. It then sums these values to given an overall levy revenue estimate for the area in the case study.
The report then use estimates of the cost of retrofitting EPC band dwellings D-E and F-G together with estimates of the number of such dwellings in each of the LSOAs to calculate the likely cost of such upgrades in each LSOA and for the whole area in the case study.
Finally the report compares the distributions of the two to understand whether sufficient revenue would be generated within each LSOA to enable the per-LSOA or whole case study area costs of the energy efficiency upgrades to be met. In doing so the report also analyses the extent to which redistribution of revenue from high emissions areas (households) would be required.
It should be noted that this area level analysis uses mean emissions per household. It will therefore not capture within-LSOA heterogeneity in emissions and so will almost certainly underestimate the range of the household level emissions levy values that might be expected.
The model uses a number of Lower Layer Super Output Area (LSOA) level datasets to analyse the patterns of emissions.
All analysis is at LSOA level. Cautions on inference from area level data apply.
## Filter: Mid Suffolk
##
## 0 1
## 32817 36
## Number of selected LSOAs - merged data:
## [1] 36
##
## Mid Suffolk
## 1 36
## Test linkage of Census 2021 data - suspect some LSOAs have changed
## c2021_nhhs c2021_pc_elec_ch_2021 IMDScore pc_fuelPoor
## Min. : 503.0 Min. : 2.212 Min. : 3.518 Min. : 4.00
## 1st Qu.: 712.0 1st Qu.: 5.180 1st Qu.: 7.989 1st Qu.: 9.75
## Median : 759.0 Median : 6.398 Median :11.421 Median :13.00
## Mean : 798.6 Mean : 7.818 Mean :12.831 Mean :12.31
## 3rd Qu.: 954.8 3rd Qu.:10.582 3rd Qu.:15.843 3rd Qu.:15.00
## Max. :1169.0 Max. :19.301 Max. :43.571 Max. :17.00
## # NAs are LSOAs that didn't match - only matters to analysis using Census 2021 data on electricity central heating
This report takes Mid Suffolk as a case study.
Figure 4.1 shows the distribution of deprivation across the case study area using the IMD 2019 data.
If there are gaps in these maps then there may be LSOA level data linking errors, possibly due to name or boundary changes. We love name and boundary changes. If so the model results may be partial. #YMMV
Figure 4.1: IMD (LSOAs)
Figure 4.2 shows the distribution of fuel poverty across the case study area using the BEIS/DESNZ 2019 data.
Figure 4.2: Fuel poverty (LSOAs)
Observed EPCs - need to check bias with respect to e.g. Census data. EPCs more likely to be from new houses, rentals, re-sales (required) etc.
## Saving 7 x 5 in image
## Number of EPC coded dwellings in case study area, (Mid Suffolk) = 16447
Imputed EPCs (see logic in Section 7.1) - this corrects for numbers but not the bias in the EPC sample
## Number of imputed EPC coded dwellings in case study area, (Mid Suffolk) = 28444
## case_study mean_MSOA_totInc
## 1: 0 43899.93
## 2: 1 45041.67
## Saving 7 x 5 in image
## Saving 7 x 5 in image
See https://www.creds.ac.uk/why-we-built-a-place-based-carbon-calculator/
“The highest carbon areas have an average per person footprint more than eight times larger than the lowest carbon areas.”
“We are not effectively targeting decarbonisation policies in high carbon areas. For example, the recently collapsed Green Homes Grants scheme provided a grant to cover 66% of the cost (up to £5,000) of retrofitting homes. For people claiming certain benefits, the cap was raised to 100% and £10,000. But the calculator shows that the big polluters are the large homes in very wealthy areas. In these neighbourhoods, the issue is not affordability but motivation. For high income households, energy costs are a small proportion of their expenditure and so the cost savings for retrofitting their home are inconsequential. As there are no policy “sticks” to incentivise action in the collective interest it is unsurprising that high carbon neighbourhoods have not prioritised decarbonisation.”
Source: https://www.carbon.place/
Notes:
region | nLSOAs | mean_KgCo2ePerCap | sd_KgCo2ePerCap |
|---|---|---|---|
East | 36 | 8,475.0 | 2,510.7 |
## Do we have any missing energy data?
LAD11NM | LSOA11CD | LSOA01NM | nElecMeters | nGasMeters | CREDS_total_kgco2e | CREDS_elec_kgco2e2018 | CREDS_gas_kgco2e2018 | CREDS_otherEnergy_kgco2e2011 | CREDS_allHomeEnergy_kgco2e2011 |
|---|
Now we need to convert the per capita to totals and then use the number of electricity meters as a proxy for the number of dwellings
Ideally we’d have Census 2021 data but we don’t have it yet. So instead we’ll use the number of electricity meters for 2018 which aligns with the CREDS data (might be an over-estimate if a dwelling has 2…)
First check the n electricity meters logic…
## LSOA11NM WD18NM nGasMeters nElecMeters epc_total
## 1: Mid Suffolk 010H Stowmarket North 990 997 788
## 2: Mid Suffolk 011B Bramford and Blakenham 893 897 713
## 3: Mid Suffolk 004C Elmswell and Norton 831 960 624
## 4: Mid Suffolk 009D Stowmarket North 830 827 531
## 5: Mid Suffolk 010F Stowmarket North 820 892 685
## 6: Mid Suffolk 010G Stowmarket North 794 789 521
## LSOA11NM WD18NM nGasMeters nElecMeters epc_total
## 1: Mid Suffolk 001A Eye 743 1068 626
## 2: Mid Suffolk 005D Stowupland 743 1048 500
## 3: Mid Suffolk 006B Haughley and Wetherden 452 999 528
## 4: Mid Suffolk 010H Stowmarket North 990 997 788
## 5: Mid Suffolk 001C Palgrave 289 971 511
## 6: Mid Suffolk 011A Barking and Somersham 372 966 461
LSOA11NM | WD18NM | nGasMeters | nElecMeters | epc_total |
|---|---|---|---|---|
Mid Suffolk 010H | Stowmarket North | 990 | 997 | 788 |
Mid Suffolk 011B | Bramford and Blakenham | 893 | 897 | 713 |
Mid Suffolk 004C | Elmswell and Norton | 831 | 960 | 624 |
Mid Suffolk 009D | Stowmarket North | 830 | 827 | 531 |
Mid Suffolk 010F | Stowmarket North | 820 | 892 | 685 |
Mid Suffolk 010G | Stowmarket North | 794 | 789 | 521 |
Check that the number of electricity meters reasonably correlates with the number of EPCs from the CREDS data. We would not expect the number of gas meters to correlate due to non-gas dwellings etc.
There may also be difficulties where there are multiple meters per property - e.g. one ‘standard’ and one ‘economy 7’. Really should switch to using address counts from postcode file.
Check that the assumption seems sensible…
Check for outliers - what might this indicate?
We want to present the analysis in ‘per dwelling’ or ‘per household’ terms so we need to convert the total kg CO2e values to per dwelling values by dividing by the number of electricity meters.
## # Summary of per dwelling values
| Name | …[] |
| Number of rows | 36 |
| Number of columns | 9 |
| Key | NULL |
| _______________________ | |
| Column type frequency: | |
| numeric | 9 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| CREDS_total_kgco2e_pdw | 0 | 1 | 19670.99 | 6604.07 | 8680.00 | 14396.31 | 19819.59 | 22264.77 | 36438.22 | ▆▆▇▂▂ |
| CREDS_gas_kgco2e2018_pdw | 0 | 1 | 1758.01 | 576.00 | 188.11 | 1735.38 | 1914.63 | 2133.35 | 2485.52 | ▁▂▁▇▆ |
| CREDS_elec_kgco2e2018_pdw | 0 | 1 | 1138.35 | 241.53 | 828.36 | 949.00 | 1077.51 | 1273.35 | 1677.92 | ▇▆▃▂▂ |
| CREDS_measuredHomeEnergy_kgco2e2018_pdw | 0 | 1 | 2896.36 | 424.05 | 1858.16 | 2693.80 | 2923.92 | 3143.81 | 3800.68 | ▁▂▇▅▂ |
| CREDS_otherEnergy_kgco2e2011_pdw | 0 | 1 | 702.87 | 767.19 | 21.41 | 136.48 | 279.07 | 1099.56 | 2642.78 | ▇▂▂▂▁ |
| CREDS_allHomeEnergy_kgco2e2018_pdw | 0 | 1 | 3599.24 | 608.40 | 2731.10 | 3058.89 | 3506.59 | 4086.86 | 4951.74 | ▇▅▂▅▂ |
| CREDS_car_kgco2e2018_pdw | 0 | 1 | 3077.07 | 728.12 | 1805.11 | 2557.34 | 3027.32 | 3581.68 | 4438.25 | ▆▆▇▇▅ |
| CREDS_van_kgco2e2018_pdw | 0 | 1 | 732.18 | 1381.74 | 92.99 | 235.23 | 452.68 | 702.73 | 8483.51 | ▇▁▁▁▁ |
| CREDS_personalTransport_kgco2e2018_pdw | 0 | 1 | 3809.25 | 1688.48 | 1941.10 | 2801.38 | 3513.12 | 4320.95 | 12018.31 | ▇▅▁▁▁ |
Examine patterns of per dwelling emissions for sense.
Figure 5.1 shows the LSOA level per dwelling ‘all emissions’ in Tonnes CO2e as estimated by the CREDS tool against the Index of Multiple Deprivation (IMD) score and uses the size of the points to represent the % of dwellings with electric heating. Colour is used to represent the IMD decile where decile 1 is the 10% least deprived.
## Per dwelling T CO2e - all emissions
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Figure 5.1: Scatter of LSOA level all consumption emissions per dwelling against IMD score
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDS_total_kgco2e_pdw
## t = -2.8274, df = 34, p-value = 0.007808
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6689572 -0.1258113
## sample estimates:
## cor
## -0.4363127
## Total emissions per dwelling (LSOA level) summary
## LSOA11CD WD18NM IMD_Decile_label All_Tco2e_per_dw
## Length:36 Length:36 6 :11 Min. : 8.68
## Class :character Class :character 8 : 6 1st Qu.:14.40
## Mode :character Mode :character 9 : 6 Median :19.82
## 10 (10% least deprived): 6 Mean :19.67
## 7 : 3 3rd Qu.:22.26
## 5 : 2 Max. :36.44
## (Other) : 2
LSOA11CD | WD18NM | IMD_Decile_label | All_Tco2e_per_dw |
|---|---|---|---|
E01030086 | Thurston and Hessett | 8 | 36.4 |
E01030070 | Ringshall | 10 (10% least deprived) | 34.1 |
E01030069 | Ringshall | 7 | 33.3 |
E01030089 | Woolpit | 9 | 26.9 |
E01030046 | Claydon and Barham | 8 | 26.4 |
E01030084 | Thurston and Hessett | 10 (10% least deprived) | 26.4 |
LSOA11CD | WD18NM | IMD_Decile_label | All_Tco2e_per_dw |
|---|---|---|---|
E01030061 | Needham Market | 6 | 8.7 |
E01030073 | Stowmarket Central | 5 | 11.1 |
E01030052 | Eye | 6 | 11.7 |
E01030077 | Stowmarket North | 3 | 11.9 |
E01030076 | Stowmarket North | 6 | 12.7 |
E01030072 | Stowmarket Central | 9 | 12.8 |
We should not be surprised that emissions are negatively correlated with deprivation. If you are, you should try reading an excellent paper by Milena Buchs and Sylke Schnepf, both formerly of the University of Southampton:
“whilst all types of emissions rise with income, low income, workless and elderly households are more likely to have high emissions from home energy than from other domains, suggesting that they may be less affected by carbon taxes on transport or total emissions. This demonstrates that fairness implications related to mitigation policies need to be examined for separate emission domains.”
With that in mind, read on.
Figure 5.2: Annual mean T CO2e per dwelling (LSOAs)
Figure 5.3 uses the same plotting method to show emissions per dwelling due to gas use.
## Per dwelling T CO2e - gas emissions
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 188.1 1735.4 1914.6 1758.0 2133.4 2485.5
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Figure 5.3: Scatter of LSOA level gas per dwelling emissions against IMD score
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDS_gas_kgco2e2018_pdw
## t = -0.68906, df = 34, p-value = 0.4955
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4293382 0.2196486
## sample estimates:
## cor
## -0.1173558
Figure 5.4 uses the same plotting method to show emissions per dwelling due to electricity use.
## Per dwelling T CO2e - elec emissions
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Figure 5.4: Scatter of LSOA level elec per dwelling emissions against IMD score - who emits?
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDS_elec_kgco2e2018_pdw
## t = -0.16629, df = 34, p-value = 0.8689
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3537303 0.3028648
## sample estimates:
## cor
## -0.02850754
Figure 5.5 uses the same plotting method to show emissions per dwelling due to other energy use. This should be higher for off-gas areas which tend to be rural areas so we also present analysis by the LSOA’s urban/rural classification.
## Per dwelling T CO2e - elec emissions
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Figure 5.5: Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits?
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDS_elec_kgco2e2018_pdw
## t = -0.16629, df = 34, p-value = 0.8689
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3537303 0.3028648
## sample estimates:
## cor
## -0.02850754
RUC11 | mean_gas_kgco2e | mean_elec_kgco2e | mean_other_energy_kgco2e |
|---|---|---|---|
Rural town and fringe | 1,934.0 | 1,116.4 | 570.8 |
Rural village and dispersed | 1,024.0 | 1,463.2 | 1,848.0 |
Urban city and town | 1,997.0 | 984.1 | 206.6 |
Check whether all measured energy emissions combined (gas & electricity) correlate with all emissions (in this data).
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$CREDS_total_kgco2e_pdw and selectedLsoasDT$CREDS_measuredHomeEnergy_kgco2e2018_pdw
## t = -0.13039, df = 34, p-value = 0.897
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3483340 0.3084445
## sample estimates:
## cor
## -0.02235676
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Do we see strong correlations? If so in theory we could (currently) use measured energy emissions as a proxy for total emissions.
Repeat for all home energy - includes estimates of emissions from oil etc
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$CREDS_total_kgco2e_pdw and selectedLsoasDT$CREDS_allHomeEnergy_kgco2e2018_pdw
## t = 3.8213, df = 34, p-value = 0.0005387
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2678272 0.7428880
## sample estimates:
## cor
## 0.5481323
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
How does the correlation look now?
Figure 5.6: Annual mean T CO2e due to energy use per dwelling (LSOAs)
We don’t expect to use this data as it is already taxed in a way that relates to emissions (?)
Figure 5.7 uses the same plotting method to show emissions per dwelling due to van use. Again, we present analysis by the LSOA’s urban/rural classification.
## Per dwelling T CO2e - car emissions
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Figure 5.7: Scatter of LSOA level car use per dwelling emissions against IMD score
## Correlation with IMD score (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDS_car_kgco2e2018_pdw
## t = -3.056, df = 34, p-value = 0.004345
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6878410 -0.1600856
## sample estimates:
## cor
## -0.4642069
RUC11 | mean_car_kgco2e | mean_van_kgco2e |
|---|---|---|
Rural town and fringe | 3,124.5 | 1,160.3 |
Rural village and dispersed | 4,021.4 | 795.5 |
Urban city and town | 2,532.3 | 327.3 |
Figure 5.8 uses the same plotting method to show emissions per dwelling due to van use.
## Per dwelling T CO2e - van emissions
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Figure 5.8: Scatter of LSOA level van use per dwelling emissions against IMD score
## Correlation with IMD score (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDS_van_kgco2e2018_pdw
## t = -0.69367, df = 34, p-value = 0.4926
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4299793 0.2189001
## sample estimates:
## cor
## -0.1181312
Figure 5.9: Mean annual T CO2e per dwelling due to transport (LSOAs)
Case studies:
BEIS/ETC Carbon ‘price’
EU carbon ‘price’
Scenario 1: apply the central value Scenario 2: apply the low/central/high as a rising block tariff for each emissions source. Set threhsolds to 33% and 66% (in absence of any other guidance!)
Table 6.1 below shows the overall £ GBP emissions levy total for the case study area in £M under Scenario 1.
nLSOAs | All emissions | Gas | Electricity |
|---|---|---|---|
36 | 133.7 | 12.1 | 7.9 |
WD20NM | nLSOAs | All emissions | Gas | Electricity |
|---|---|---|---|---|
Thurston | 4 | 17.6 | 1.1 | 0.9 |
Stow Thorney | 3 | 14.2 | 1.4 | 0.6 |
Elmswell & Woolpit | 3 | 13.5 | 1.3 | 0.7 |
Combs Ford | 4 | 12.0 | 1.5 | 0.7 |
Claydon & Barham | 3 | 11.5 | 0.8 | 0.6 |
Chilton | 4 | 11.4 | 1.4 | 0.7 |
Onehouse | 2 | 9.5 | 0.2 | 0.6 |
Blakenham | 2 | 8.6 | 0.6 | 0.5 |
Needham Market | 3 | 7.7 | 1.1 | 0.5 |
Haughley, Stowupland & Wetherden | 2 | 7.1 | 0.7 | 0.6 |
Bramford | 2 | 5.8 | 0.7 | 0.4 |
Battisford & Ringshall | 1 | 4.9 | 0.3 | 0.1 |
Palgrave | 1 | 4.7 | 0.2 | 0.4 |
Eye | 1 | 3.1 | 0.5 | 0.3 |
St Peter's | 1 | 2.2 | 0.4 | 0.2 |
WD20NM | nLSOAs | All emissions | Gas | Electricity |
|---|---|---|---|---|
Thurston | 4 | 17.6 | 1.1 | 0.9 |
Stow Thorney | 3 | 14.2 | 1.4 | 0.6 |
Elmswell & Woolpit | 3 | 13.5 | 1.3 | 0.7 |
Combs Ford | 4 | 12.0 | 1.5 | 0.7 |
Claydon & Barham | 3 | 11.5 | 0.8 | 0.6 |
Chilton | 4 | 11.4 | 1.4 | 0.7 |
Onehouse | 2 | 9.5 | 0.2 | 0.6 |
Blakenham | 2 | 8.6 | 0.6 | 0.5 |
Needham Market | 3 | 7.7 | 1.1 | 0.5 |
Haughley, Stowupland & Wetherden | 2 | 7.1 | 0.7 | 0.6 |
Bramford | 2 | 5.8 | 0.7 | 0.4 |
Battisford & Ringshall | 1 | 4.9 | 0.3 | 0.1 |
Palgrave | 1 | 4.7 | 0.2 | 0.4 |
Eye | 1 | 3.1 | 0.5 | 0.3 |
St Peter's | 1 | 2.2 | 0.4 | 0.2 |
Figure 6.1: Emissions level due to total emissions, gas & electricity use by LSOA (Scenario 1)
The table below shows the mean per dwelling value rounded to the nearest £10.
All_emissions | Gas | Electricity | Gas + Electricity |
|---|---|---|---|
4,819.4 | 430.7 | 278.9 | 709.6 |
Figure ?? shows the total £k per LSOA and £ per dwelling revenue using BEIS central carbon price plotted against IMD score. The tables show the LSOAs with the highest and lowest values.
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Figure 6.2: £k per LSOA revenue using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Figure 6.3: £k per LSOA revenue using BEIS central carbon price
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2127 3527 4856 4819 5455 8927
LSOA11CD | LSOA01NM | WD18NM | T_CO2e_pdw | GBP_allEmissions_levy | msoa_tot_annual_income_2018 |
|---|---|---|---|---|---|
E01030086 | Mid Suffolk 006D | Thurston and Hessett | 36.4 | 8,927.4 | 45,000 |
E01030070 | Mid Suffolk 011E | Ringshall | 34.1 | 8,342.3 | 42,500 |
E01030069 | Mid Suffolk 009B | Ringshall | 33.3 | 8,149.5 | 48,800 |
E01030089 | Mid Suffolk 006E | Woolpit | 26.9 | 6,593.0 | 45,000 |
E01030046 | Mid Suffolk 012D | Claydon and Barham | 26.4 | 6,461.6 | 43,900 |
E01030084 | Mid Suffolk 004D | Thurston and Hessett | 26.4 | 6,460.4 | 50,100 |
LSOA11CD | LSOA01NM | WD18NM | T_CO2e_pdw | GBP_allEmissions_levy | msoa_tot_annual_income_2018 |
|---|---|---|---|---|---|
E01030061 | Mid Suffolk 011C | Needham Market | 8.7 | 2,126.6 | 42,500 |
E01030073 | Mid Suffolk 008A | Stowmarket Central | 11.1 | 2,709.1 | 36,900 |
E01030052 | Mid Suffolk 001A | Eye | 11.7 | 2,862.6 | 44,600 |
E01030077 | Mid Suffolk 008B | Stowmarket North | 11.9 | 2,926.7 | 36,900 |
E01030076 | Mid Suffolk 010D | Stowmarket North | 12.7 | 3,109.3 | 47,500 |
E01030072 | Mid Suffolk 009C | Stowmarket Central | 12.8 | 3,135.0 | 48,800 |
Figure ?? repeats the analysis but just for gas.
Anything unusual?
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Figure 6.4: £k per LSOA incurred via gas using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Figure 6.5: £k per LSOA incurred via gas using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 46.09 425.17 469.09 430.71 522.67 608.95
LSOA11CD | LSOA01NM | WD18NM | gas_T_CO2e_pdw | GBP_gas_levy_perdw | msoa_tot_annual_income_2018 |
|---|---|---|---|---|---|
E01030086 | Mid Suffolk 006D | Thurston and Hessett | 2.5 | 609.0 | 45,000 |
E01030045 | Mid Suffolk 012C | Claydon and Barham | 2.4 | 580.5 | 43,900 |
E01030071 | Mid Suffolk 010B | Stowmarket Central | 2.3 | 562.7 | 47,500 |
E01030044 | Mid Suffolk 012B | Bramford and Blakenham | 2.3 | 555.4 | 43,900 |
E01030080 | Mid Suffolk 008D | Stowmarket South | 2.2 | 542.8 | 36,900 |
E01030075 | Mid Suffolk 009D | Stowmarket North | 2.2 | 540.1 | 48,800 |
LSOA11CD | LSOA01NM | WD18NM | gas_T_CO2e_pdw | GBP_gas_levy_perdw | msoa_tot_annual_income_2018 |
|---|---|---|---|---|---|
E01030041 | Mid Suffolk 011A | Barking and Somersham | 0.9 | 232.6 | 42,500 |
E01030064 | Mid Suffolk 001C | Palgrave | 0.9 | 225.0 | 44,600 |
E01030063 | Mid Suffolk 009A | Onehouse | 0.8 | 206.7 | 48,800 |
E01030046 | Mid Suffolk 012D | Claydon and Barham | 0.8 | 191.9 | 43,900 |
E01030049 | Mid Suffolk 004B | Elmswell and Norton | 0.2 | 49.5 | 50,100 |
E01030069 | Mid Suffolk 009B | Ringshall | 0.2 | 46.1 | 48,800 |
Figure ?? repeats the analysis for electricity.
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Figure 6.6: £k per LSOA incurred via electricity using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Figure 6.7: £k per LSOA incurred via electricity using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 202.9 232.5 264.0 278.9 312.0 411.1
LSOA11CD | LSOA01NM | WD18NM | elec_T_CO2e_pdw | GBP_elec_levy_perdw | msoa_tot_annual_income_2018 |
|---|---|---|---|---|---|
E01030049 | Mid Suffolk 004B | Elmswell and Norton | 1.7 | 411.1 | 50,100 |
E01030069 | Mid Suffolk 009B | Ringshall | 1.7 | 409.2 | 48,800 |
E01030046 | Mid Suffolk 012D | Claydon and Barham | 1.6 | 389.0 | 43,900 |
E01030064 | Mid Suffolk 001C | Palgrave | 1.5 | 368.5 | 44,600 |
E01030063 | Mid Suffolk 009A | Onehouse | 1.5 | 365.4 | 48,800 |
E01030041 | Mid Suffolk 011A | Barking and Somersham | 1.5 | 361.5 | 42,500 |
LSOA11CD | LSOA01NM | WD18NM | elec_T_CO2e_pdw | GBP_elec_levy_perdw | msoa_tot_annual_income_2018 |
|---|---|---|---|---|---|
E01033316 | Mid Suffolk 010F | Stowmarket North | 0.9 | 223.0 | 47,500 |
E01030077 | Mid Suffolk 008B | Stowmarket North | 0.9 | 222.3 | 36,900 |
E01030075 | Mid Suffolk 009D | Stowmarket North | 0.9 | 216.6 | 48,800 |
E01033317 | Mid Suffolk 010G | Stowmarket North | 0.9 | 212.6 | 47,500 |
E01033319 | Mid Suffolk 010H | Stowmarket North | 0.9 | 211.2 | 47,500 |
E01030043 | Mid Suffolk 011B | Bramford and Blakenham | 0.8 | 202.9 | 42,500 |
Figure ?? shows the same analysis for measured energy (elec + gas)
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Figure 6.8: £k per LSOA incurred via electricity and gas using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Figure 6.9: £k per LSOA incurred via electricity and gas using BEIS central carbon price
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 455.2 660.0 716.4 709.6 770.2 931.2
Applied to per dwelling values (not LSOA total) - may be methodologically dubious?
Cut at 25%, 50% - so any emissions over 50% get high carbon cost
## Cuts for total per dw
## 0% 25% 50% 75% 100%
## 8.68000 14.39631 19.81959 22.26477 36.43822
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Table: (#tab:estimateAnnualLevyScenario2Total)Data summary
| Name | …[] |
| Number of rows | 36 |
| Number of columns | 3 |
| Key | NULL |
| _______________________ | |
| Column type frequency: | |
| numeric | 3 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| V1 | 0 | 1 | 19.67 | 6.60 | 8.68 | 14.40 | 19.82 | 22.26 | 36.44 | ▆▆▇▂▂ |
| beis_GBPtotal_sc2_perdw | 0 | 1 | 3417.37 | 2021.09 | 1058.96 | 1780.63 | 3087.38 | 3982.44 | 9184.09 | ▇▇▃▁▂ |
| beis_GBPtotal_sc2 | 0 | 1 | 2572267.85 | 1233482.22 | 931884.80 | 1457415.69 | 2562540.90 | 3228759.17 | 5311898.69 | ▇▃▇▂▂ |
Gas cuts:
## Cuts for gas per dw
## 0% 25% 50% 75% 100%
## 0.1881099 1.7353820 1.9146345 2.1333505 2.4855212
## [1] 7.135033
## Cuts for elec per dw
## 0% 10% 20% 30% 40% 50% 60% 70% 80%
## 0.8283612 0.8955987 0.9302606 0.9547021 1.0079111 1.0775103 1.1423873 1.2473264 1.3324725
## 90% 100%
## 1.4977594 1.6779155
## [1] 5.571227
LSOA11CD | LSOA01NM | WD18NM | T_CO2e_pdw | GBP_allEmissions_levy |
|---|---|---|---|---|
E01030086 | Mid Suffolk 006D | Thurston and Hessett | 36.4 | 9,184.1 |
E01030070 | Mid Suffolk 011E | Ringshall | 34.1 | 8,307.7 |
E01030069 | Mid Suffolk 009B | Ringshall | 33.3 | 8,018.9 |
E01030089 | Mid Suffolk 006E | Woolpit | 26.9 | 5,687.3 |
E01030046 | Mid Suffolk 012D | Claydon and Barham | 26.4 | 5,490.5 |
E01030084 | Mid Suffolk 004D | Thurston and Hessett | 26.4 | 5,488.7 |
LSOA11CD | LSOA01NM | WD18NM | T_CO2e_pdw | GBP_allEmissions_levy |
|---|---|---|---|---|
E01030061 | Mid Suffolk 011C | Needham Market | 8.7 | 1,059.0 |
E01030073 | Mid Suffolk 008A | Stowmarket Central | 11.1 | 1,349.0 |
E01030052 | Mid Suffolk 001A | Eye | 11.7 | 1,425.4 |
E01030077 | Mid Suffolk 008B | Stowmarket North | 11.9 | 1,457.4 |
E01030076 | Mid Suffolk 010D | Stowmarket North | 12.7 | 1,548.3 |
E01030072 | Mid Suffolk 009C | Stowmarket Central | 12.8 | 1,561.1 |
LSOA11CD | LSOA01NM | WD18NM | T_CO2e_pdw | GBP_gasEmissions_levy |
|---|---|---|---|---|
E01030086 | Mid Suffolk 006D | Thurston and Hessett | 2.5 | 465.1 |
E01030045 | Mid Suffolk 012C | Claydon and Barham | 2.4 | 422.6 |
E01030071 | Mid Suffolk 010B | Stowmarket Central | 2.3 | 395.8 |
E01030044 | Mid Suffolk 012B | Bramford and Blakenham | 2.3 | 384.9 |
E01030080 | Mid Suffolk 008D | Stowmarket South | 2.2 | 366.0 |
E01030075 | Mid Suffolk 009D | Stowmarket North | 2.2 | 362.0 |
LSOA11CD | LSOA01NM | WD18NM | T_CO2e_pdw | GBP_gasEmissions_levy |
|---|---|---|---|---|
E01030069 | Mid Suffolk 009B | Ringshall | 0.2 | 22.9 |
E01030049 | Mid Suffolk 004B | Elmswell and Norton | 0.2 | 24.7 |
E01030046 | Mid Suffolk 012D | Claydon and Barham | 0.8 | 95.6 |
E01030063 | Mid Suffolk 009A | Onehouse | 0.8 | 102.9 |
E01030064 | Mid Suffolk 001C | Palgrave | 0.9 | 112.0 |
E01030041 | Mid Suffolk 011A | Barking and Somersham | 0.9 | 115.8 |
LSOA11CD | LSOA01NM | WD18NM | T_CO2e_pdw | GBP_elecEmissions_levy |
|---|---|---|---|---|
E01030049 | Mid Suffolk 004B | Elmswell and Norton | 1.7 | 392.1 |
E01030069 | Mid Suffolk 009B | Ringshall | 1.7 | 389.3 |
E01030046 | Mid Suffolk 012D | Claydon and Barham | 1.6 | 359.0 |
E01030064 | Mid Suffolk 001C | Palgrave | 1.5 | 328.3 |
E01030063 | Mid Suffolk 009A | Onehouse | 1.5 | 323.7 |
E01030041 | Mid Suffolk 011A | Barking and Somersham | 1.5 | 317.9 |
LSOA11CD | LSOA01NM | WD18NM | T_CO2e_pdw | GBP_elecEmissions_levy |
|---|---|---|---|---|
E01030043 | Mid Suffolk 011B | Bramford and Blakenham | 0.8 | 101.1 |
E01033319 | Mid Suffolk 010H | Stowmarket North | 0.9 | 105.2 |
E01033317 | Mid Suffolk 010G | Stowmarket North | 0.9 | 105.9 |
E01030075 | Mid Suffolk 009D | Stowmarket North | 0.9 | 107.8 |
E01030078 | Mid Suffolk 010E | Stowmarket South | 0.9 | 109.3 |
E01030077 | Mid Suffolk 008B | Stowmarket North | 0.9 | 112.1 |
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
nLSOAs | All emissions | Gas | Electricity |
|---|---|---|---|
36 | 92.6 | 7.1 | 5.6 |
WD20NM | nLSOAs | All emissions | Gas | Electricity |
|---|---|---|---|---|
Thurston | 4 | 15.2 | 0.7 | 0.8 |
Stow Thorney | 3 | 10.1 | 0.9 | 0.3 |
Elmswell & Woolpit | 3 | 9.6 | 0.8 | 0.5 |
Claydon & Barham | 3 | 8.8 | 0.5 | 0.5 |
Onehouse | 2 | 7.7 | 0.1 | 0.6 |
Combs Ford | 4 | 7.2 | 0.9 | 0.4 |
Chilton | 4 | 6.7 | 0.8 | 0.4 |
Blakenham | 2 | 5.3 | 0.3 | 0.4 |
Battisford & Ringshall | 1 | 4.8 | 0.1 | 0.1 |
Needham Market | 3 | 4.3 | 0.7 | 0.3 |
Bramford | 2 | 3.8 | 0.4 | 0.2 |
Haughley, Stowupland & Wetherden | 2 | 3.6 | 0.4 | 0.5 |
Palgrave | 1 | 3.0 | 0.1 | 0.3 |
Eye | 1 | 1.5 | 0.3 | 0.3 |
St Peter's | 1 | 1.1 | 0.2 | 0.1 |
Figure 6.10: Total levy due to all emissions, gas & electricity use by LSOA (Scenario 2)
The table below shows the mean per dwelling value rounded to the nearest £10.
All_emissions | Gas | Electricity |
|---|---|---|
833.8 | 255.5 | 195.5 |
Figure 6.11 compares the % £ levy under each scenario for all consumption contributed by LSOAs in each IMD decile.
nLSOAs | sum_total_sc1 | sum_total_sc2 |
|---|---|---|
36 | 133.7 | 92.6 |
Figure 6.11: Comparing £ levy under each scenario by IMD decile - all consumption emissions
## Saving 7 x 5 in image
Figure 6.12 compares the £ levy under each scenario for all consumption.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
Figure 6.12: Comparing £ levy under each scenario - all consumption emissions
Source: English Housing Survey 2018 Energy Report
Model excludes EPC A, B & C (assumes no need to upgrade)
Adding these back in would increase the cost… obvs
There are likely to be a range of biases in the EPC sample such as:
Check distributions of EPCs against known building stock:
## pc_missing_epcs
## Min. :20.51
## 1st Qu.:38.69
## Median :44.42
## Mean :42.65
## 3rd Qu.:48.67
## Max. :53.71
These plots suggest:
In order to estimate the LSOA level retrofit costs, we need to impute the EPC counts in each LSOA. We do this using the number of electricity meters as the presumed number of dwellings and the observed % of EPCs in each band for all dwellings with EPCs which is provided by the CREDS data. We simply upweight the EPC counts proportionally so that the total matches the number of electricity meters.
Note that this assumes that if we had EPCs for all dwellings then the % in each band in each LSOA would stay the same. This is quite a bold assumption given what we have seen above but there is currently no way to re-weight them (e.g. to fit the dwelling type counts) with the data we have - we do not know the of EPCs of each Band that come from different dwelling types.
Note to self: this could be done by re-weighting the case level EPC data using Census outcomes at LSOA level - e.g. via IPF
Note that the EPC database is continuously updated so more recent upgrades will not be captured in the data used for this analysis. This means the total retrofit costs are likely to be an over-estimate. The extent of this over-estimate would require the use of an updated (current) EPC data extract and is left for future work.
## N EPCs
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 266.0 351.2 450.0 456.9 521.0 788.0
## N elec meters
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 518.0 707.2 754.5 790.1 906.2 1068.0
## [1] 100
## [1] 100
variable | nDwellings | pc | i.nDwellings | i.pc |
|---|---|---|---|---|
nDwellings_A | 74.79 | 0.27 | 12,737.80 | 0.17 |
nDwellings_B | 4,633.15 | 16.62 | 778,932.47 | 10.59 |
nDwellings_C | 8,746.14 | 31.38 | 2,208,676.15 | 30.04 |
nDwellings_D | 9,719.33 | 34.87 | 2,939,176.13 | 39.98 |
nDwellings_E | 3,695.68 | 13.26 | 881,518.85 | 11.99 |
nDwellings_F | 686.00 | 2.46 | 502,229.00 | 6.83 |
nDwellings_G | 320.59 | 1.15 | 28,665.51 | 0.39 |
Correlation between high % EPC F/G or A/B and deprivation?
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Now we need to convert the % to dwellings using the number of electricity meters (see above).
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Upgrade all D-G:
Source: estimates of the cost of retrofitting dwellings in different EPC bands ‘to an appropriate standard’ published in a recent English Housing Survey technical report (MHCLG 2018) (see https://journal-buildingscities.org/articles/10.5334/bc.279#T2)
The model does not upgrade A-C as it was assumed that dwellings in the least energy-efficient EPC bands (D–G) would be prioritised and that EPC bands A–C would not be retrofitted since they would already conform to the current UK policy objective of:
“as many homes as possible to achieve EPC band C by 2035 where cost-effective, practical and affordable, and to ensure as many fuel poor homes as reasonably practicable achieve a band C rating by the end of 2030.” https://www.gov.uk/government/news/plan-to-drive-down-the-cost-of-clean-heat
Table 7.1 reports total retofit costs.
## To retrofit D-E (£m)
## [1] 178.4197
## Number of dwellings: 13415
## To retrofit F-G (£m)
## [1] 42.20769
## Number of dwellings: 1575
## To retrofit D-G (£m)
## [1] 220.6274
## To retrofit D-G (mean per dwelling)
## [1] 14554.16
Mean total £m per LSOA | Total £m |
|---|---|
6.1 | 220.6 |
Ward | Mean total £m per LSOA | Total £m |
|---|---|---|
Stowmarket North | 3.3 | 19.9 |
Stowmarket Central | 6.5 | 19.5 |
Elmswell and Norton | 6.4 | 19.3 |
Claydon and Barham | 6.4 | 19.2 |
Thurston and Hessett | 5.8 | 17.5 |
Needham Market | 5.7 | 17.1 |
Stowmarket South | 5.7 | 17.0 |
Bramford and Blakenham | 4.6 | 13.8 |
Palgrave | 11.2 | 11.2 |
Ringshall | 5.4 | 10.9 |
Barking and Somersham | 10.2 | 10.2 |
Eye | 9.8 | 9.8 |
Haughley and Wetherden | 9.6 | 9.6 |
Onehouse | 9.3 | 9.3 |
Stowupland | 8.5 | 8.5 |
Woolpit | 7.7 | 7.7 |
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Figure 7.1 shows the LSOA level retofit costs per dwelling by IMD decile.
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Figure 7.1: LSOA level retofit costs per dwelling by IMD score
## Saving 7 x 5 in image
Figure 7.2: Total estimated retorfit costs per LSOA
Set aside areas in IMD decile 1 & 2? Assume costs met by HM Treasury or elsewhere e.g. energy levy etc
to do
Totals
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Repeat per dwelling
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Totals
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Repeat per dwelling
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Figure 9.1 shows years to pay under Scenario 1 (all emissions)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.655 2.556 2.992 3.365 4.236 6.801
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Figure 9.1: Years to pay under Scenario 1 (all em issions)
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Median years: 2.99
Figure 9.2 maps the distribution of years to pay for all LSOAs in the case study.
Figure 9.2: Years to pay per LSOA (Scenbario 1, all emissions levy, no redistribution)
Figure 9.3 shows years to pay under Scenario 1 (energy emissions)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 15.86 18.12 19.98 21.12 21.80 36.19
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Figure 9.3: Years to pay under Scenario 1 (energy emissions)
## Median years: 19.98
Figure 9.4 shows those LSOAs which would be in surplus (above the y = 0 line) after Year 1.
Figure 9.4: Surplus £ after year 1 for each LSOA (all emissions levy)
Figure 9.5 shows the same distribution but for the energy emissions levy. Clearly there is a lower level of surplus due to the reduced levy value at LSOA level.
Figure 9.5: Surplus £ after year 1 for each LSOA (energy levy)
What happens in Year 2 totally depends on the rate of upgrades… given the supply chain & capacity issues it’s likely that the levy would build up a substantial ‘headroom’ that could then be spent over time…
Figure 9.6 shows the year 1 outcome at ward level if levy is shared equally across all LSOAs (all emissions levy - orange dots).
Figure 9.6: Year 1 outcome if levy is shared equally (all emissions levy)
## Saving 7 x 5 in image
LSOA | LSOA name | Ward | Retrofit cost £m | Years to pay (all emissions levy) | Years to pay (energy emissions levy) | % EPC D | % EPC E | % EPC F | % EPC G |
|---|---|---|---|---|---|---|---|---|---|
E01030064 | Mid Suffolk 001C | Palgrave | 11.2 | 3.3 | 27.0 | 36.4 | 21.1 | 10.6 | 3.9 |
E01030041 | Mid Suffolk 011A | Barking and Somersham | 10.2 | 3.3 | 26.4 | 34.9 | 20.4 | 9.1 | 2.8 |
E01030052 | Mid Suffolk 001A | Eye | 9.8 | 5.7 | 21.0 | 28.3 | 15.2 | 10.2 | 2.4 |
E01030056 | Mid Suffolk 006B | Haughley and Wetherden | 9.6 | 4.3 | 26.5 | 35.4 | 16.1 | 8.1 | 2.3 |
E01030063 | Mid Suffolk 009A | Onehouse | 9.3 | 3.3 | 26.5 | 35.3 | 19.7 | 7.9 | 1.0 |
E01030081 | Mid Suffolk 005D | Stowupland | 8.5 | 4.5 | 21.7 | 29.8 | 16.8 | 6.0 | 1.2 |
E01030089 | Mid Suffolk 006E | Woolpit | 7.7 | 2.3 | 20.3 | 34.2 | 15.0 | 5.6 | 1.0 |
E01030049 | Mid Suffolk 004B | Elmswell and Norton | 7.6 | 2.6 | 34.1 | 35.0 | 19.2 | 9.4 | 2.3 |
E01030071 | Mid Suffolk 010B | Stowmarket Central | 7.4 | 4.1 | 18.3 | 36.6 | 16.9 | 7.9 | 2.2 |
E01030042 | Mid Suffolk 012A | Bramford and Blakenham | 7.2 | 2.9 | 19.9 | 37.6 | 17.2 | 7.3 | 2.9 |
Figure 9.7 shows years to pay under Scenario 2 (all emissions)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.609 3.357 4.786 5.725 8.477 13.658
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Figure 9.7: Years to pay under Scenario 2 (all emissions)
## Median years: 4.79
Figure 9.8 shows years to pay under Scenario 2 (energy emissions)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 20.40 28.45 34.73 33.29 37.25 45.55
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Figure 9.8: Years to pay under Scenario 2 (energy emissions)
Figure 9.9 shows those LSOAs which would be in surplus (above the y = 0 line) after Year 1.
Figure 9.9: Surplus £ after year 1 for each LSOA (Scenario 2, all emissions levy)
Figure 9.10 shows the same distribution for the Scenario 2 energy levy.
Figure 9.10: Surplus £ after year 1 for each LSOA (Scenario 2, all emissions levy)
What happens in Year 2 totally depends on the rate of upgrades…
Figure 9.11 shows the year 1 outcome at ward level if levy is shared equally across all LSOAs (all emissions levy - orange dots).
Figure 9.11: Year 1 outcome if levy is shared equally (Scenario 2, all emissions levy)
## Saving 7 x 5 in image
LSOA | LSOA name | Ward | Retrofit cost £m | Years to pay (all emissions levy) | Years to pay (energy emissions levy) | % EPC D | % EPC E | % EPC F | % EPC G |
|---|---|---|---|---|---|---|---|---|---|
E01030064 | Mid Suffolk 001C | Palgrave | 11.2 | 5.2 | 36.4 | 36.4 | 21.1 | 10.6 | 3.9 |
E01030041 | Mid Suffolk 011A | Barking and Somersham | 10.2 | 5.2 | 36.2 | 34.9 | 20.4 | 9.1 | 2.8 |
E01030052 | Mid Suffolk 001A | Eye | 9.8 | 11.5 | 33.0 | 28.3 | 15.2 | 10.2 | 2.4 |
E01030056 | Mid Suffolk 006B | Haughley and Wetherden | 9.6 | 8.6 | 39.4 | 35.4 | 16.1 | 8.1 | 2.3 |
E01030063 | Mid Suffolk 009A | Onehouse | 9.3 | 5.3 | 35.6 | 35.3 | 19.7 | 7.9 | 1.0 |
E01030081 | Mid Suffolk 005D | Stowupland | 8.5 | 9.1 | 38.6 | 29.8 | 16.8 | 6.0 | 1.2 |
E01030089 | Mid Suffolk 006E | Woolpit | 7.7 | 2.6 | 33.4 | 34.2 | 15.0 | 5.6 | 1.0 |
E01030049 | Mid Suffolk 004B | Elmswell and Norton | 7.6 | 3.2 | 37.6 | 35.0 | 19.2 | 9.4 | 2.3 |
E01030071 | Mid Suffolk 010B | Stowmarket Central | 7.4 | 7.8 | 26.1 | 36.6 | 16.9 | 7.9 | 2.2 |
E01030042 | Mid Suffolk 012A | Bramford and Blakenham | 7.2 | 3.9 | 31.3 | 37.6 | 17.2 | 7.3 | 2.9 |
Figure 9.12 compares pay-back times for the two scenarios - who does the rising block tariff help?
Figure 9.12: Comparing pay-back times across scenarios
## Saving 7 x 5 in image
Table 10.1 shows the summary overall levy value results for Mid Suffolk by Scenario.
Scenario | nLSOAs | All emissions levy | Gas emissions levy | Electricity emissions levy |
|---|---|---|---|---|
Scenario 1 | 36 | 133.7 | 12.1 | 7.9 |
Scenario 2 | 36 | 92.6 | 7.1 | 5.6 |
Table 10.2 shows the summary overall retrofit cost results for Mid Suffolk.
Total retrofit cost £m | Mean total retrofit cost £m per LSOA |
|---|---|
220.6 | 6.1 |
As a reminder:
The following table (Table 10.3) summarises all results by ward.
WD20NM | nLSOAs | All emissions levy (£m, Scenario 1) | Gas emissions levy (£m, Scenario 1) | Electricity emissions levy (£m, Scenario 1) | All emissions levy (£m, Scenario 2) | Gas emissions levy (£m, (Scenario 2) | Electricity emissions levy (£m, Scenario 2) | Total retrofit cost (£m) | Mean total retrofit cost per LSOA (£m) |
|---|---|---|---|---|---|---|---|---|---|
Stowmarket North | 19.9 | 3.3 | |||||||
Stowmarket Central | 19.5 | 6.5 | |||||||
Elmswell and Norton | 19.3 | 6.4 | |||||||
Claydon and Barham | 19.2 | 6.4 | |||||||
Thurston and Hessett | 17.5 | 5.8 | |||||||
Needham Market | 3 | 7.7 | 1.1 | 0.5 | 4.3 | 0.7 | 0.3 | 17.1 | 5.7 |
Stowmarket South | 17.0 | 5.7 | |||||||
Bramford and Blakenham | 13.8 | 4.6 | |||||||
Palgrave | 1 | 4.7 | 0.2 | 0.4 | 3.0 | 0.1 | 0.3 | 11.2 | 11.2 |
Ringshall | 10.9 | 5.4 | |||||||
Barking and Somersham | 10.2 | 10.2 | |||||||
Eye | 1 | 3.1 | 0.5 | 0.3 | 1.5 | 0.3 | 0.3 | 9.8 | 9.8 |
Haughley and Wetherden | 9.6 | 9.6 | |||||||
Onehouse | 2 | 9.5 | 0.2 | 0.6 | 7.7 | 0.1 | 0.6 | 9.3 | 9.3 |
Stowupland | 8.5 | 8.5 | |||||||
Woolpit | 7.7 | 7.7 |
As ever, #YMMV.